Wearable health monitors and methods of monitoring health

ABSTRACT

Wearable technologies, such as wearable health monitors, and methods of use are provided. In some embodiments, the wearable technology can be worn at the wrist of an individual and can use an accelerometer, pulse oximeter, and electrocardiogram to measure heart rate, oxygen saturation, blood pressure, pulse wave velocity, and activity. This information can then be provided to the individual. The individual can alter their behaviors and relationships with their own health by using features such as notifications and auto-tagging to better understand their own stress, diet, sleep, and exercise levels over various time periods and subsequently make appropriate behavioral changes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/768,363, filed Apr. 13, 2018, and entitled“WEARABLE HEALTH MONITORS AND METHODS OF MONITORING HEALTH”, which is a371 application of International Patent Application Ser. No.PCT/CA2016/051195, filed Oct. 13, 2016, and entitled “WEARABLE HEALTHMONITORS AND METHODS OF MONITORING HEALTH”, which claims the benefit ofU.S. Provisional Patent Application Ser. No. 62/240,607, filed Oct. 13,2015, the entire disclosures of which applications are herebyincorporated herein by references.

FIELD OF THE DISCLOSURE

The present disclosure is related to wearable technologies and, moreparticularly, to wearable health monitors and methods of monitoringhealth.

BACKGROUND

There has been a decline in the overall health status of the generalpopulation. Metabolic syndrome (i.e., the combined effects of obesity,diabetes, hypertension, and dyslipidemia) and its components have beenincreasing throughout the past twenty years, mostly due to worseningdiets, poor exercise levels, personal stress, and reduced or poor sleeppatterns. Additionally, individuals have poor relationships with theirown health.

There have been attempts to address these issues by providing the healthmonitoring of an individual. Existing attempts, however, have theirshortcomings and the behavior of the monitored individuals remainunchanged. In most cases, insufficient information is provided to theindividual, the information is not communicated to the individual in anappropriate/efficient way, the individual may not easily relate orunderstand how the information impacts them personally, and/or theindividual is not able to provide input into the system in order tocustomize the information. As such, the individual's behavior remainsunchanged and without behavioral change, the health and/or well-being ofthe individual does not improve or, in many cases, worsens.

Accordingly, there remains a need to provide health monitors and methodsof monitoring health and wellness that can overcome the shortcomings ofthe prior art.

SUMMARY

Wearable technologies, such as wearable health monitors, and methods ofuse are provided. In some embodiments, the wearable technology can beworn at the wrist of an individual and can use an accelerometer, pulseoximeter, and electrocardiogram to measure heart rate, oxygensaturation, blood pressure, pulse wave velocity, and activity. Thisinformation can then be provided to the individual. The individual canalter their behaviors and relationships with their own health by usingfeatures such as notifications and auto-tagging to better understandtheir own stress, diet, and exercise levels over various time periodsand subsequently make appropriate behavioral changes.

In some embodiments, a wearable electronic apparatus is provided, theapparatus to be worn around an individual's wrist in order to monitorand assess the individual's cardiovascular health, the apparatuscomprising: a band comprising an electronics module configured toposition the electronics module proximate a distal radial artery portionof the individual's wrist, the electronics module an accelerometer fortaking accelerometer readings from the wrist to be converted into anactivity rating; and an electrocardiogram (ECG) and a pulse oximetersensor with photoplethysmography (PPG) for taking heart rate, pulse wavevelocity (PWV), and oxygen saturation readings from the wrist to beconverted into blood pressure ratings; a processor module incommunication with the electronics module to implement an algorithm toconvert the PWV readings into estimated blood pressure measurements andfor converting the readings and ratings into outputs (health assessmentsand/or health recommendations); and an output/display module incommunication with the processor module for reflecting any of thereadings, ratings, and/or outputs to the individual.

In some embodiments, the apparatus can be retrofit to existing wearabletechnologies, for example, the apparatus can comprise a wristbandperipheral, to be retrofit to a watch, a smartwatch, or othercomplimentary wrist-worn wearable technology.

In some embodiments, the apparatus can be activated or deactivated bythe individual contacting the electronics module with one finger fromthe opposite hand that the wrist is being worn on. The contact of thefinger on one side of the apparatus in combination with the contact ofthe wrist with the other side of the apparatus can complete anelectronic circuit.

In some embodiments, the output of the data readings and analysis canprovide a health trajectory for the individual to predict a future stateof health, as well as review the historical health trajectory from paststates.

In some embodiments, the output of the data readings and analysis can besubject to an “autotagging” program where the apparatus can determinethe individual's behavior (such as eating, sleeping, exercising, or astate of stress) at a time point and attaching an electronic tag to thatevent. The auto-tagging program can also include a means whereby manualdata can also augment and increase the individual's ability to captureadditional details of personal relevance.

In some embodiments, the data collected and amalgamated through theapparatus can be subject to “machine learning” methodologies to providefor predictive analysis for the individual to assist the individual inachieving personal health and wellness objectives.

In some embodiments, the data collected by the apparatus can applypsychometrics data analysis to assist the individual in achievingpersonal health and wellness objectives.

Broadly stated, in some embodiments, a wearable electronic apparatus isprovided, the apparatus to be worn around an individual's wrist in orderto monitor and assess the individual's cardiovascular health, theapparatus comprising: a band comprising an electronics module, the bandconfigured to position the electronics module proximate a distal radialartery portion of the individual's wrist, the electronics modulecomprising, an accelerometer for taking accelerometer readings from thewrist to be converted into an activity rating; and an electrocardiogram(ECG) and a pulse oximeter sensor with photoplethysmography (PPG) fortaking heart rate, pulse wave velocity (PWV), pulse transit time (PTT),and oxygen saturation readings from the wrist to be converted into bloodpressure ratings; a processor module in communication with theelectronics module to implement an algorithm to convert the ECG and PWVreadings into blood pressure and for converting the readings and ratingsinto outputs (health assessments and/or health recommendations); and anoutput/display module in communication with the processor module forreflecting any of the readings, ratings, and/or outputs to theindividual.

In some embodiments, the output/display module is integral with theelectronics module. In some embodiments, the output/display module isremote, but in communication with the electronics module. In someembodiments, the output/display module is a multi-color LED forproviding individual interaction with device. In some embodiments, themulti-color LED is configured to let the individual know if ECG, pulseoximeter, and accelerometer are ready to obtain the readings, if theapparatus is converting the readings to the outputs, and if theapparatus has successfully completed each measurement. In someembodiments, the band further comprises a connection to receive asmartwatch. In some embodiments, the connection is a smartwatch pinconnection.

Broadly stated, in some embodiments, a method is provided for monitoringand assessing an individual's cardiovascular health, the steps of themethod comprising providing a wearable electronic apparatus as describedherein; positioning the electronics module proximate the distal radialartery portion of a wrist of an individual; taking simultaneousmeasurements of activity, heart rate, PWV, and oxygen saturation; usingthe processor to convert the heart rate and PWV measurements into ablood pressure reading; and reflecting any of the readings, ratings,and/or outputs to the individual through the output/display module.

In some embodiments, the taking of the simultaneous measurements step isaccomplished by using a finger of one hand of the individual to touchthe wearable electronic apparatus on wrist of other hand to complete theECG circuit and record the simultaneous measurements. In someembodiments, the output of the data readings and analysis can be subjectto an auto-tagging and/or tagging program where the apparatus candetermine the individual's behavior as an event (such as eating,sleeping, working out, or stress) at a time point and attach anelectronic/digital tag to that event. In some embodiments, the output ofthe data readings and further analysis can provide a health trajectoryfor the individual to predict a future state of health.

According to one aspect of this disclosure, there is provided a wearableelectronic apparatus for monitoring a user's cardiovascular health. Theapparatus comprises: an attachment structure for attaching the wearableapparatus to a predefined body location of the user; and an electronicsmodule coupled to the attachment structure. The electronics modulecomprises: a plurality of sensors comprising at least anelectrocardiogram (ECG) sensor for measuring data including at least oneof the heart rate, pulse wave velocity (PWV), pulse transit time (PTT),and a pulse oximeter sensor with photoplethysmography (PPG) formeasuring oxygen saturation of the user; a processor module coupled tothe plurality of sensors for collecting data therefrom and forcalculating blood pressure using a machine learning method based atleast one collected data; and an output module coupled to the processormodule outputting at least one of the calculated blood pressure and thecollected data.

In some embodiments, the pulse oximeter sensor comprises a Pulse LED anda light sensor.

In some embodiments, the Pulse LED is configured to emit a first lightat a first wavelength and a second light at a second wavelength, andwherein the light sensor is configured to receive at least a portion ofthe first light reflected from a skin of the user and at least a portionof the second light reflected from a skin of the user.

In some embodiments, the first wavelength is about 660 nm and the secondwavelength is about 940 nm.

In some embodiments, the electronics module further comprises anaccelerometer for collecting the movement data of the user.

In some embodiments, the processor module calculates blood pressureusing the machine learning method based on collected data and the user'shistorical health data.

In some embodiments, the machine learning method is a polynomialregression analysis method, a neural network, a Bayesian network, adecision tree, an adaptive logic network, or a support vector machines.

In some embodiments, the attachment structure is a band.

In some embodiments, the band is configured to position the wearableelectronic apparatus on a wrist of the user.

In some embodiments, the band is configured to position the electronicsmodule in proximity with a blood vessel of the user.

In some embodiments, the blood vessel is a distal radial artery.

In some embodiments, the output module is a display on the wearableelectronic apparatus.

In some embodiments, the display is a multi-color LED.

In some embodiments, the display is configured to notify the user if theplurality of sensors are ready to obtain readings, if the wearableelectronic apparatus is converting the readings to output data, and ifthe wearable electronic apparatus has successfully completed collectionof data from the sensors.

In some embodiments, the output module comprises a communicationcomponent for transmitting at least one of the calculated blood pressureand the collected data to a device external to the wearable electronicapparatus.

In some embodiments, the communication component is a wirelesscommunication module.

In some embodiments, the wireless communication module is a Bluetoothmodule.

In some embodiments, the attachment structure further comprises aconnection for receiving a smartwatch.

In some embodiments, the connection is a pin connection.

In some embodiments, the electronics module further comprises at leastone circuit board, a battery and a battery charging circuit.

According to another aspect of this disclosure, there is provided amethod for monitoring a user's cardiovascular health. The methodcomprises: collecting data of the user using at least one sensor, saiddata comprising at least one of the heart rate, pulse wave velocity(PWV), pulse transit time (PTT), and oxygen saturation; calculatingblood pressure of the user using a machine learning method based atleast one collected data; and outputting at least one of the calculatedblood pressure and the collected data.

In some embodiments, the collecting data step comprises: collecting atleast one of the heart rate, pulse wave velocity (PWV), pulse transittime (PTT) of the user using at least an electrocardiogram (ECG) sensor;and collecting oxygen saturation of the user using a pulse oximetersensor with photoplethysmography (PPG).

In some embodiments, collecting oxygen saturation of the user using apulse oximeter sensor comprises: emitting a first light at a firstwavelength and a second light at a second wavelength; receiving at leasta portion of the first light reflected from a skin of the user and atleast a portion of the second light reflected from a skin of the user;determining a first reading corresponding to the amount of the firstlight being absorbed by the blood under the skin and a second readingcorresponding to the amount of the second light being absorbed by theskin; and calculating the oxygen saturation of the user using the ratiobetween the first and second readings.

In some embodiments, the first wavelength is about 660 nm and the secondwavelength is about 940 nm.

In some embodiments, the above method further comprises: collecting themovement data of the user using an accelerometer.

In some embodiments, the calculating blood pressure step comprises:calculating blood pressure using the machine learning method based oncollected data and the user's historical health data.

In some embodiments, the machine learning method is a polynomialregression analysis method, a neural network, a Bayesian network, adecision tree, an adaptive logic network, or a support vector machines.

In some embodiments, the above method further comprises: attaching theat least one sensor on a wrist of the user.

In some embodiments, the attaching step comprises: attaching the atleast one sensor on the wrist of the user using a band.

In some embodiments, the attaching step comprises: attaching the atleast one sensor on the wrist of the user in proximity with a bloodvessel of the user.

In some embodiments, the blood vessel is a distal radial artery.

In some embodiments, the outputting step comprises: displaying at leastone of the calculated blood pressure and the collected data.

In some embodiments, the displaying step comprises: displaying at leastone of the calculated blood pressure and the collected data using amulti-color LED.

In some embodiments, the displaying step comprises: notifying the userif the at least one sensor is ready to obtain readings, if the readingsare being converted to output data, and if collection of data from thesensors has been successfully completed.

In some embodiments, the outputting step comprises: transmitting atleast one of the calculated blood pressure and the collected data to aremote device. In some embodiments, the transmitting step comprises:wirelessly transmitting at least one of the calculated blood pressureand the collected data to the remote device.

In some embodiments, the transmitting step comprises: transmitting atleast one of the calculated blood pressure and the collected data to theremote device using Bluetooth.

In some embodiments, the above method further comprises: attaching theat least one sensor about a smartwatch.

In some embodiments, attaching the at least one sensor about thesmartwatch comprises: attaching the at least one sensor about thesmartwatch using a pin connection.

According to another aspect of this disclosure, there is provided asystem for monitoring a user's cardiovascular health. The systemcomprises: an attachment structure for attaching the wearable apparatusto a predefined body location of the user; an electronics module coupledto the attachment structure; and a device external to the attachmentstructure. The electronics module comprises: a plurality of sensorscomprising at least an electrocardiogram (ECG) sensor for measuring dataincluding at least one of the heart rate, pulse wave velocity (PWV),pulse transit time (PTT), and a pulse oximeter sensor withphotoplethysmography (PPG) for measuring oxygen saturation of the user;a processor module coupled to the plurality of sensors for collectingdata therefrom and; and an output module coupled to the processor moduleoutputting at least one of collected data to the external device. Theexternal device calculates blood pressure using a machine learningmethod based at least one data received from the output module.

In some embodiments, the pulse oximeter sensor comprises a Pulse LED anda light sensor.

In some embodiments, the Pulse LED is configured to emit a first lightat a first wavelength and a second light at a second wavelength, andwherein the light sensor is configured to receive at least a portion ofthe first light reflected from a skin of the user and at least a portionof the second light reflected from a skin of the user.

In some embodiments, the first wavelength is about 660 nm and the secondwavelength is about 940 nm.

In some embodiments, the electronics module further comprises anaccelerometer for collecting the movement data of the user.

In some embodiments, the processor module calculates blood pressureusing the machine learning method based on collected data and the user'shistorical health data.

In some embodiments, the machine learning method is a polynomialregression analysis method, a neural network, a Bayesian network, adecision tree, an adaptive logic network, or a support vector machines.

In some embodiments, the attachment structure is a band.

In some embodiments, the band is configured to position the wearableelectronic apparatus on a wrist of the user.

In some embodiments, the band is configured to position the electronicsmodule in proximity with a blood vessel of the user.

In some embodiments, the blood vessel is a distal radial artery.

In some embodiments, the output module is a display on the wearableelectronic apparatus.

In some embodiments, the display is a multi-color LED.

In some embodiments, the display is configured to notify the user if theplurality of sensors are ready to obtain readings, if the wearableelectronic apparatus is converting the readings to output data, and ifthe wearable electronic apparatus has successfully completed collectionof data from the sensors.

In some embodiments, wherein the output module comprises a communicationcomponent for transmitting at least one of the calculated blood pressureand the collected data to a device external to the wearable electronicapparatus.

In some embodiments, the communication component is a wirelesscommunication module.

In some embodiments, the wireless communication module is a Bluetoothmodule.

In some embodiments, the attachment structure further comprises aconnection for receiving a smartwatch.

In some embodiments, the connection is a pin connection.

In some embodiments, the electronics module further comprises at leastone circuit board, a battery and a battery charging circuit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of an embodiment of a wearable electronicapparatus for monitoring and assessing an individual's cardiovascularhealth;

FIG. 2 is a schematic diagram 140 showing the structure of theelectrical components of the wearable apparatus;

FIG. 3A is a front perspective view of an embodiment of an electronicsmodule for a wearable electronic apparatus for monitoring and assessingan individual's cardiovascular health;

FIG. 3B is a rear perspective view of the embodiment of the electronicsmodule shown in FIG. 2 ;

FIGS. 4A and 4B show a front elevation view of embodiments of anelectronic App for monitoring and assessing an individual'scardiovascular health, as depicted on an embodiment of a smartphone orother display device; and

FIG. 5 is a diagram representation of embodiments of algorithms that canbe used in association with a wearable electronic apparatus formonitoring and assessing an individual's cardiovascular health.

DETAILED DESCRIPTION

Wearable technologies, such as wearable health monitors, and methods ofuse are provided.

In some embodiments, the wearable technology can be worn at the wrist ofan individual and can use an accelerometer, pulse oximeter, andelectrocardiogram to measure heart rate, oxygen saturation, bloodpressure, pulse wave velocity, and activity. This information can thenbe provided to the individual. The individual can alter their behaviorsand relationships with their own health by using features such asnotifications and auto-tagging and/or manual tagging, machine learning,artificial intelligence, psychometric analysis, and predictiveanalytics, to better understand their own stress, diet, and exerciselevels over various time periods and subsequently make appropriatebehavioral changes.

Referring now to FIG. 1 , an embodiment of a wearable apparatus is shownand generally referred using the numeral 100. The wearable apparatuscomprises an electronics module 102 and an attachment structure 104 suchas a band for attaching the wearable apparatus 100 to a user's wrist. Inthis embodiment, the electronics module 102 is housed within a hard case106, and comprises a circuit board, for example a printed circuit board(PCB) 108 for collecting data from a plurality of sensors (describedlater) for monitoring a user's health metrics.

The band 104 in this embodiment is soft or flexible case, and may houseother components of apparatus 100. For example, in this embodiment, theband 104 comprises openings for a light sensor 110 housed therein, andone or more sensors such as an electrocardiogram (ECG) electrodes 112for acquiring a signal of electrical activity through the user's heart.In this embodiment, the band 104 comprises two ECG electrodes 112, onetouching an anterior portion of the user's wrist, and the other to betouched by the user's opposing hand's finger to complete a circuitacross the heart for make a reading.

For example, the user can use a finger of one hand to touch theapparatus 100 that is attached to the wrist of the opposite arm in orderto record an ECG signal, and simultaneously initiate a complete set ofbiometrics as described later. As band 104 can be configured to bepositioned on a user's wrist, simultaneous quality measurements of bloodpressure, pulse wave velocity, pulse transit time, and oxygen saturationcan also be provided.

The band 104 is configured and sized to position electronics module 102proximate the distal portion of a user's radial artery in order toachieve increased accuracy in electronic readings. In some embodiments,the band 104 is an expandable band for adapting to different sizes ofwrists. For example, one or more link pieces 116 may be added to theband 104 to extend the band 104 for larger size wrist.

In some embodiments, the two ends 120 and 122 of the band 104, or theend 120 of the band 104 and the end 124 of the link piece 116, may becoupled to each other wearable apparatus 100 for attaching the wearableapparatus 100 to a user's wrist. In some other embodiments, the band 104may be configured to receive a smartwatch (not shown) through aconnection 126, for example, but not limited to a smartwatch pinconnection to connect to the smartwatch. In some embodiments, connection126 can be configured to attach to a pre-existing smartwatch female pinconnection. In some embodiments, connection 126 can be integrally builtinto band 104. Accordingly, the wearable apparatus 100 and smartwatchcan be worn as a single wearable device, wherein the apparatus 100 actsas a smartband, namely a band comprising smart electronics or aconnection to communication with a smart technology, or acts as aperipheral to a smartwatch that can be configured to tie-in withexisting smartwatches for the purpose of adding key health metrics thatdo not currently exist on such smartwatches. Where a health metric, suchas the heart rate and pulse measurements, can be provided by thesmartwatch, the wearable apparatus 100 can provide an additionalmeasurement to compare to the smartwatch measurement.

FIG. 2 is a schematic diagram 140 showing the structure of theelectrical components of the wearable apparatus 100. As shown, in thisembodiment, the wearable apparatus 100 comprises a processor 142, alight sensor 110, an ECG module 145 connecting to the ECG electrodes112, pulse LED 144, a wireless communication module 146 such as aBluetooth module, a memory 148, a battery 150, a charging circuit 152for charging the battery 150, an accelerometer 154, and necessarycircuitry such as a digital circuitry on the PCB 108. The process 142 isin communication with the components 110, and 144 through 154, andcontrols the operation thereof. The wearable apparatus 100 may alsocomprise a Bluetooth antenna (not shown) for transmitting and receivingBluetooth signals.

Referring now to FIGS. 3A and 3B, an embodiment of the PCB 108 is shownfrom the front and rear respectively. As shown, the PCB 108 comprisesECG inputs 182 to attach ECG electrodes 112 to the digital circuitry184. The PCB 108 also comprises LED Indicators 186 to attach one or moreLEDs, e.g., four (4) LEDs 186 in the example, for informing the userregarding the status or states of the apparatus 100, such as charging,measurement acquisition, transfer to app processes, and the like. Insome embodiments, multi-color LEDs can be used for facilitating userinteraction with the apparatus 100. The LEDs 186 are used for indicatingto the user if ECG, pulse oximeter, and accelerometer sensors are ableto get readings and if the device is converting those readingmeasurements to the appropriate outputs. Accordingly, feedback onadequacy of the measurements can be provided to the user.

In this embodiment, the PCB 108 comprises, on its front side, a primaryenergy input 186 for connecting to an power source (not shown), e.g., abattery such as a Lithium battery, for powering the components 110, and142 through 154 of the apparatus 100. In this embodiment, the PCB 108further comprises a secondary energy input, such as a charging circuitry152 and a USB charger input 188, connecting to a secondary power source(not shown) as needed, for charging the battery. In another embodiment,the wearable apparatus 100 may also be powered by the secondary powersource via the secondary energy input such that the wearable apparatus100 is still operable when the battery is being charged.

In this embodiment, the processor 142, the Bluetooth module 146 andmemory 148 are integrated into one central chip 190 such as amicrocontroller, also mounted to the front side of the PCB 108.

On its rear side, the PCB 108 comprises a Pulse LED 144 to provide twodifferent light sources, with one at a wavelength of about 660 nm (redlight) and the other at a wavelength of about 940 nm (infrared light),and a light sensor 110 to acquire the light emitted from the Pulse LED144 and reflected off of a peripheral artery. In this embodiment, lightsensor 110 also comprises two separate light sensing componentscorresponding to the two light sources of the Pulse LED 144, includingone light sensing component at about 660 nm and another light sensingcomponent at about 940 nm. Alternatively, the light sensor 110 may be asingle sensor capable of acquiring both 660 nm and 940 nm wavelengths.The Pulse LED 144 and light sensor 110 can be used for pulse oximetry,forming a pulse oximeter with photoplethysmography (PPG). Pulse oximetryworks by comparing a first reading corresponding to the amount of lightabsorbed by the blood under the skin at one wavelength, for exampleabout 660 nm, corresponding to oxygenated blood, and a second readingcorresponding to the amount of light absorbed by the blood under theskin at a second wavelength, for example about 940 nm, corresponding todeoxygenated blood. The ratio between the two readings can determine howmuch oxygen is present in the user's blood and can also called oxygensaturation.

In this embodiment, the Pulse LED 144 and light sensor 110 are adjacentto each other on the PCB 108, and are located in the wearable apparatus100 such that, when a user wears the wearable apparatus 100 on a wristthereof, the Pulse LED 144 and light sensor 110 are in proximity withthe radial artery. In this manner, the light emitted from the Pulse LED144 is reflected by the skin close to the radial artery and received bythe light sensor 110. The light received by the light sensor 110 thencontains more detailed information about the user's health metrics,compared to that when the Pulse LED 144 and light sensor 110 are atother locations of the wearable apparatus 100.

In some embodiments, the PCB 108 can also include an accelerometer 154for activity tracking by assessing the movement that the apparatus 100experiences. In some embodiments, the accelerometer 154 can beincorporated into the central chip 190. In some embodiments, anaccelerometer measurement can be acquired from an external accelerometersuch as from a connected smartwatch that has a built-in accelerometer.

In this embodiment, the processor 142 executes machine executable codeimplementing a built-in device algorithm for computing basic healthmetrics such as heart rate. In some embodiments, the code of the devicealgorithm is stored in the memory 148. The memory 148 also stores basicdata collected from the above described sensors.

As described above, the central chip 190 integrates a Bluetoothcomponent 146. The Bluetooth component 146 transmits data to and fromexternal devices such as smartphones, tablets, laptops, desktops and thelike.

In operation, the wearable apparatus 100 can be configured to provide auser with feedback regarding their health metrics and health behaviors.In particular, the wearable apparatus 100 collects data or measurementsfrom the pulse oximeter, ECG, and accelerometer to provide heart rate,oxygen saturation, pulse wave velocity, blood pressure, and activitymeasurements.

In some embodiments, the sensor measurements can be inputted into thecircuitry on the PCB 108 via the ECG inputs 182 from the ECG electrodes112, from the light sensor 110 receiving reflected light from the PulseLED 144, and the accelerometer 154 (or an external accelerometer asdescribed above). Such sensor measures may be stored in the memory 148,and may be used by the processor 142 for basic computing using thedevice algorithm to obtain basic health metrics such as heart rate.

In some embodiments, ECG and pulse oximetry readings are combined toobtain the pulse wave velocity (PWV), which can then be used tocalculate a blood pressure value. In order to do this the R-wave of theECG signal can be compared with the onset of the blood pulse sensed bythe pulse oximeter. In some embodiments, a portion of the devicealgorithm can help to find these two points differentiated in time.Known estimates of distances between a person's heart and wrist fromanthropometric data can help determine the distance between these twopositions. By dividing the distance over the time, the PWV can becalculated. Then, various factors including, but not limited to, age,gender, smoking history, alcohol history, blood sugar control, activity,sleep, heart rate, and diet can be factored into an estimate of theuser's blood pressure.

In one embodiment, the processor 142 uses an algorithm implementing amethod described in the academic paper “Continuous blood pressuremeasurement by using the pulse transit time: comparison to a cuff-basedmethod” to Heiko Gesche, Detlef Grosskurth, Gert Kuchler and AndreasPatzak, published on Eur J Appl Physiol. 2012 January; 112 (1):309-15,the content of which is incorporated herein by reference in itsentirety. The method uses Pulse transit time (PTT) and pulse wavevelocity (PWV) for monitoring blood pressure.

In some embodiments, the wearable apparatus 100 uses machine learning toprocess the data collected from various sensors to provide forpredictive analysis for the user to assist the user in achievingpersonal health and wellness objectives.

For example, the wearable apparatus 100 uses a supervised learningmethod for calculating and predicting blood pressure as follows.

In this method, data is in sets of PTT and Other Markers. The methoduses a suitable machine learning method, such as machine learning methodmay be a polynomial regression analysis method, a neural network, aBayesian network, a decision tree, an adaptive logic network, a supportvector machines or the like, for function learning. Training output foreach recording includes a value for Systolic blood pressure and a valuefor Diastolic blood pressure.

In predicting blood pressure using PTT and other markers, the vascularsystem follows a physical model of a pump that pumps a fluid of aparticular viscosity into a tube of a particular length and width with aspecific wall elasticity, the amount of time it takes for a fluidpressure wave to travel through the tube can be calculated by theMoens-Korteweg equation:PWV=sqrt((E _(inc) *h)/2rρ)where sqrt(x) representing the square root of x, E_(inc) is theincremental modulus of stiffness, h is vessel wall thickness, r is thevessel radius, ρ is the density of blood, and PWV is the pulse wavevelocity.

Based on the Moens-Korteweg equation, a probabilistic model may beestablished using PTT and Other Markers as probabilistic predictors ofSystolic blood pressure (SBP) and diastolic blood pressure (DBP). SBPand DBP are random variables, the values of which are determined byother variables include PTT, height, weight, ethnicity, age, gender,Pulse Wave Characteristics and the like. Herein, PTT is the amount oftime it takes for the heart to start pumping and for the pump pulse toreach the radial artery. Ethnicity is a number or index used todistinguish different data sets. Pulse Wave Characteristics include avariety of values derived from standard PPG pulse wave analysis.

Pulse wave analysis is known, for example, the academic paper “On theAnalysis of Fingertip Photoplethysmogram Signals”, to Mohamed Elgendi,published on Current Cardiology Reviews, 2012, 8, 14-25, the content ofwhich is incorporated herein by reference in its entirety, provides adetailed description of pulse wave analysis.

By using pulse wave analysis, basic pulse wave characteristics may beobtained, including the systolic peak, diastolic peak, peak difference,dicrotic notch, and pulse width. In this embodiment, the supervisedlearning method uses these characteristics along with Pulse TransitTime, Heart Rate, Heart Rate Variability, and optionally other recordedvalues to generate SBP vs DBP values based on a machine learned mappingfunction. Using more data increases the accuracy and reliability of theblood pressure calculation.

The method uses ECG to detect the start of the heart beat and PPG todetect the point at which the pulse reaches the radial artery. While theECG or PPG measuring location in the method disclosed herein isrelatively far from the heart, Applicant's test results show that, themethod is insensitive to the specific distance between the ECG or PPGmeasuring location and the heart, or the specific point at which the ECGor PPG is measured, as long as the measurement is consistent, themeasurement results or readings are about the same for the same personat about the same body conditions during different measurements.

In one embodiment, the polynomial regression analysis may be used togenerate a mapping function. As is known in the art and described inWikipedia, polynomial regression is a statistic method with a form oflinear regression in which the relationship between the independentvariable x and the dependent variable y is modelled as an n-th degreepolynomial. Polynomial regression fits a nonlinear relationship betweenthe value of x and the corresponding conditional mean of y, denotedE(y|x), and has been used to describe nonlinear phenomena. Theregression function E(y|x) is linear in the unknown parameters that areestimated from the data.

Those skilled in the art appreciate that, in various embodiments, anymachine learning technique will work to produce a viable function map.

The supervised learning method assumes a particular polynomial degreeand attempts to learn an equation by considering multiple examples.

For example, for EBP using 3 unknowns,EBP=A*PTT*2+B*PTT+C,where A, B and C are unknowns. Given 3 examples that include an EBPvalue and a PTT value, the values of A, B and C are then solved.

The supervised learning method uses multiple training points to find acloser fit. In order to make the function more reliable regression canbe used to consider multiple points along the curve.

As those skilled in the art appreciate, polynomial regression analysisis one form of function fitting, and other machine learning algorithmscan be alternatively used to generate alternative function maps. Forexample, in another embodiment, neural network analysis may be used.

As defined by Dr. Robert Hecht-Nielsen in “Neural Network Primer: PartI” by Maureen Caudill, AI Expert, February 1989, a neural network is acomputing system made up of a number of simple, highly interconnectedprocessing elements, which process information by their dynamic stateresponse to external inputs. Neural networks are typically organized inlayers. Layers are made up of a number of interconnected “nodes”containing an “activation function”.

In the neural network analysis disclosed herein, the retinal layer isrepresented by the inputs. The function map is then modeled usingmultiple hidden layers. The final layer may be modeled as a collectionof neurons representing the range of output. Alternatively, the finallayer may be a single neuron with an output intensity that maps to aparticular DBP or SBP value.

In some embodiments, the wearable apparatus 100 may be configured tosend the sensor readings/measurements and processor-calculated datathrough the Bluetooth 146 to external devices such as smartphones,tablets, laptops, desktops and the like. As shown in FIGS. 4A and 4B, anApp 202 or a software application is executed on a display device 200 toprovide an interactive user experience. The App 202 receives the sensormeasurements and processor-calculated data from the wearable apparatus100 using Bluetooth paired to the Bluetooth 146 of the wearableapparatus 100, and display some or all of the received sensormeasurements and processor-calculated data on the display. The App 202may further calculate extended data, e.g., a Pulse Score 204 and PulsePoints 206, related to body conditions, health, activity targets and thelike, and display the calculated data 206 on the display.

A Pulse Score 204 can be a scoring method to provide an indication tothe user as a sense of the general state of their health, as an example,of where their health is compared relative to others. Pulse Score 204can also allow the user to see how their behavior changes impact theirown health. In some embodiments, improvements (for example, an increase)in Pulse Score 204, reflecting an improvement in health, are a way userscan attain Pulse Points 206. Pulse Points 206 are a rewards system wherean individual can use Pulse Score 204 to accumulate Pulse Points 206,which in turn, can provide, or be exchanged for, rewards for meetinggoals. Accordingly, as the user improves/increases their Pulse Score 204through making more sound health decisions, they can receive PulsePoints 206 that can be redeemed for prizes or discounts on purchases,e.g., purchases of health related items.

In some embodiments, the App 202 implements a portable algorithm, forproviding the user a reflection of total health status by taking thebasic outputs from the wearable apparatus 100 and converting that datainto heart rate, oxygen saturation, pulse wave velocity, blood pressure,and activity.

FIG. 5 shows the device algorithm 220, the portable algorithm 228 andthe interaction therewith. As shown, the sensors 222 of the wearableapparatus 100, such as the ECG module 145, the Pulse Oximeter (Pulse LED144 and light sensor 110) and the accelerometer 154, collects data ofthe user. The processor 142 uses the collected data to calculate theuser's body and activity data 224 such as pulse wave velocity, heartrate, oxygen saturation and activity. The calculated body and activitydata 224 may be output, e.g., displayed, at the output 226 of theapparatus 100. The output 226 may be an LCD/LED display on or integratedwith the wearable apparatus 100 for displaying the output data in textor graphic form, or one or more LEDs (which may be considered as asimple display) on or integrated with the wearable apparatus 100 fordisplaying the output data using different combination of LED lights. Inthis embodiment, the wearable apparatus may not comprise a Bluetoothmodule 146.

Alternatively, the output data may be transmitted to the App 202 of adevice 200 external or remote to the wearable apparatus 100 via, e.g.,Bluetooth. The App 202 executes the portable algorithm 228, and uses theuser's information 230 such as the user's historical health data, e.g.,diabetes status, age, gender, smoking history, alcohol history and thelike, to calculate blood pressure 232.

In some embodiments, the App 202 also comprises a tagging function 234allowing automatically tagging (Auto-tagging) and/or manually tagging,where the output of the data readings and analysis can be subject to atagging program where apparatus 100 can determine the user's behavior236, such as eating, sleeping, working out, or is stressed, at a timepoint and attaching an electronic tag to that event.

Tagging 234 can be understood to be the taking of core health metricsfrom apparatus 100 and using analytics to determine if the user isinvolved in such behaviors 236 as eating, sleeping, resting, workingout, or is stressed at a specific time and attaching a digital tag tothat event.

Auto-tagging can be understood to be the automatic tagging of theseevents by the App 202. In some embodiments, the measures of heart rate,activity, pulse wave velocity, oxygen saturation, and/or blood pressurein combination with prior- or post-tagging can be used to accomplishauto-tagging, or to provide additional personal input or information. Inorder to adequately provide auto-tagging options heart rate and activitytracking can be used to be able to tell resting, exercising, andsleeping states. In some embodiments, the use of pulse wave velocity canallow for determination of fatty meals the user may have had and thisinformation can be used for tagging/auto-tagging. An indication ofstress can be found through changes in blood pressure and heart ratewith minimal movement and this information can be used fortagging/auto-tagging.

In some embodiments, auto-tagging functions can be combined with ahealth trajectory output for the individual to predict a future state ofhealth. These two features can provide the user with the reflection andforesight to start making better health decisions such as increasedexercise, a better diet, improved sleeping habits, or increasedawareness and response to stressors.

Accordingly, apparatus 100 and the methods of using the same can providefor the acquisition of more useful health metrics, a more convenient anduser-friendly way to acquire measurements, and a mechanism to providefeedback to the user to generate thought and behavior change of the userwith respect to their own health. The user can be provided withpertinent information that will help them make more informed healthdecisions. In some-embodiments, these health metrics can be captured asreal-time information, at regular or pre-set intervals over the courseof time, or at any time that the user wishes to apply.

In above embodiments, the wearable apparatus 100 collects data of theradial artery for calculating health metrics. However, in somealternative embodiments, the wearable apparatus 100 does not requiredata of the radial artery. Rather, the wearable apparatus 100 may locateits sensor(s) at any suitable body location along an artery where apulse is detectable. For example, in various embodiments, the wearableapparatus 100 may locate its sensor(s) at a thumb, finger, ankle, toe,forehead and the like. For example, in one embodiment, the wearableapparatus 100 may be in the form of a headband.

In above embodiments, the wearable apparatus 100 comprises a single PCB108. In some alternative embodiments, the wearable apparatus 100comprises a plurality of rigid circuit boards 108 electrically coupledvia suitable flexible electrical cables or wires. For example, in oneembodiment, the wearable apparatus 100 comprises three rigid PCBs, witha first PCB receiving the charging circuit 152 and the accelerometer154, a second PCB receiving the central chip 190 and the Bluetoothantenna, and a third PCB receiving the sensors and/or connectors ofsensors, such as the ECG input 182 and the Pulse LED 144 and lightsensor 110. In this embodiment, the second PCB is intermediate of thefirst and third PCBs.

Compared to the use of a single flexible circuit board, using multiplerigid circuit boards provides several advantages. For example, the rigidcircuit boards provide sufficient protection to the components thereon.Further, by using above-described multiple PCBs 108, the electroniccircuitry, including the PCBs 108 and components thereon, can be madewith a small size and/or thickness for easily fitting into a rigid case106.

In another embodiment, the wearable apparatus 100 is designed in amodularized manner for the ease of installing and replacing sensors. Forexample, the Pulse LED 144 and light sensor 110 in this embodiment areon a separate circuit board, which is connected to the PCB 108 viarespective connectors on the circuit boards, and a flexible cabletherebetween. The connectors are also modularized with standardized pinsfor connecting to different Pulse LEDs and light sensors.

In above embodiments, a suitable machine learning method is used tocalculate blood pressure based some or all of the collected data and theuser's historical health data. In an alternative embodiment, the bloodpressure is calculated using the machine learning method based only onsome or all of the collected data. No historical health data of the useris used. While the resulting blood pressure may not be as accurate asthat calculated based on both the collected data and the user'shistorical health data, the resulting blood pressure may still havesufficient accuracy for the user to use.

In above embodiments, the sensors are arranged about an artery such as adistal radial artery. In some alternative embodiments, the sensors arearranged about a blood vessel, which may be a vein or an artery,depending on the implementation.

In above embodiments, the pulse oximeter comprises a pulse LED as alight emitter. In some other embodiments, any suitable light emittersmay be alternatively used.

In above embodiments, the pulse LED or light emitter is used fordetecting blood pulse wave and for detecting oxygen saturation, and theECG sensor is used for detecting the start of pulse. In some alternativeembodiments, a sonic sensor is used for detecting blood pulse wave. Insome of these embodiments, no light emitter is used. However, a drawbackof these embodiments is that the oxygen saturation may be undetectable.

Although a few embodiments have been shown and described, it will beappreciated by those skilled in the art that various changes andmodifications might be made without departing from the scope of theinvention. The terms and expressions used in the preceding specificationhave been used herein as terms of description and not of limitation, andthere is no intention in the use of such terms and expressions ofexcluding equivalents of the features shown and described or portionsthereof, it being recognized that the invention is defined and limitedonly by the claims that follow.

While the above description details certain embodiments of the inventionand describes certain embodiments, no matter how detailed the aboveappears in text, the invention can be practiced in many ways. Details ofthe apparatuses and methods may vary considerably in theirimplementation details, while still being encompassed by the inventiondisclosed herein. These and other changes can be made to the inventionin light of the above description.

Particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific embodimentsdisclosed in the specification. Accordingly, the actual scope of theinvention encompasses not only the disclosed embodiments, but also allequivalent ways of practicing or implementing the invention.

The above description of the embodiments of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above or to the particular field of usage mentioned in thisdisclosure. While specific embodiments of, and examples for, theinvention are described above for illustrative purposes, variousequivalent modifications are possible within the scope of the invention,as those skilled in the relevant art will recognize. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

While certain aspects of the invention are presented below in certainclaim forms, the inventors contemplate the various aspects of theinvention in any number of claim forms. Accordingly, the inventorsreserve the right to add additional claims after filing the applicationto pursue such additional claim forms for other aspects of theinvention.

REFERENCES

The following references are hereby incorporated by reference into thisapplication in their entirety:

-   -   1. The Reference Values for Arterial Stiffness Collaboration,        “Determinants of pulse wave velocity in healthy people and in        the presence of cardiovascular risk factors: ‘establishing        normal and reference values’”. European Heart Journal (2010) 31,        2338-2350 doi: 10.1093/euroheart/ehq165.    -   2. Augustine et al., “Effect of a Single Bout of Resistance        Exercise on Arterial Stiffness Following a High-Fat Meal”, Int J        Sports Med; DOI: http://dx.doi.org/10.1055/s-0033-1363266        Copyright Georg Thieme Verlag, Stuttgart, New York (2014); ISSN        0172-4622.    -   3. Millasseau et al., “Determination of age-related increases in        large artery stiffness by digital pulse contour analysis”,        Clinical Science (2002) 103, 371-377.    -   4. Firstbeat Technologies Ltd., “Stress and Recovery Analysis        Method Based on 24-hour Heart Rate Variability”, Published Sep.        16, 2014, updated Nov. 4, 2014.    -   5. Contini, R, “Body Segment Parameters, Part II”, Artificial        Limbs (1972), 16 (1) 1-19.

What is claimed is:
 1. An apparatus, comprising: a strap; a circuitboard attached to the strap and comprising a plurality of sensors,wherein the plurality of sensors comprises respective sensors configuredto sense electrocardiogram (ECG) data and photoplethysmography (PPG)data from a user; and a computing system communicatively coupled to thecircuit board and configured to predict a health metric of the user byusing machine learning and using the sensed ECG data and the sensed PPGdata as inputs for the machine learning, wherein the machine learningcomprises a neural network, wherein the machine learning furthercomprises one or any combination of a support vector machine and apolynomial regression analysis, wherein the circuit board comprises alight emitting diode (LED) and a light sensor, wherein the LED isconfigured to emit a first type of light and a second type of light,wherein the light sensor comprises respective components to sense thefirst type of light and the second type of light, wherein the first typeof light has wavelengths within a first range of wavelengths and thesecond type of light has wavelengths within a second range ofwavelengths, wherein the strap comprises openings, wherein the firsttype of light and the second type of light, or corresponding derivativesthereof, pass through the openings prior to being sensed by the lightsensor, wherein the light sensor and the LED are parts of a pulseoximeter that comprises the sensor configured to sense the PPG data,wherein the pulse oximeter comprises a computing component configuredto: compare a first reading sensed by the light sensor corresponding toan amount of the first type of light absorbed by blood of the user underskin of the user, corresponding to oxygenated blood, and a secondreading sensed by the light sensor corresponding to an amount of thesecond type of light absorbed by the blood, corresponding todeoxygenated blood; and determine an amount of oxygen in the blood basedon the comparison between the first reading and the second reading, andwherein the computing system is configured to predict the health metricof the user using the determined amount of oxygen in the blood as one ofthe inputs for the machine learning.
 2. The apparatus of claim 1,comprising: a first connector at a first end of the strap; and a secondconnector at a second end of the strap, the second connector configuredto connect to the first connector to fasten the first end to the secondend, directly, to configure the strap into a wristband, or indirectly,to configure the strap with a smartwatch into a smart wristwatch.
 3. Theapparatus of claim 1, further comprising: a battery charging circuit;and a microcontroller comprising the computing system, wherein themicrocontroller is intermediate to the plurality of sensors and thecharging circuit.
 4. The apparatus of claim 1, wherein the health metriccomprises a blood pressure of the user.
 5. The apparatus of claim 1,wherein the circuit board comprises: a first ECG electrode facinginwardly when the strap is configured into a wristband or a wristwatchto allow the first ECG electrode to touch an anterior portion of thewrist of the user; and a second ECG electrode arranged to allow thesecond ECG electrode to be touched by a finger of an opposing hand ofthe user to complete a circuit across a heart of the user to produce anelectrical output to make an ECG reading, when the strap is configuredinto a wristband or a wristwatch, wherein the computing system isconfigured to predict the health metric of the user using the ECGreading as an input for the machine learning.
 6. The apparatus of claim1, wherein a final layer of the neural network consists of a singleneuron or a group of neurons each with an output intensity that maps tothe health metric.
 7. The apparatus of claim 1, wherein training outputof the machine learning comprises an instance of the health metric foreach combination of inputs of the ECG reading and the amount of oxygenin the blood.
 8. The apparatus of claim 1, wherein the machine learningcomprises supervised learning.
 9. The apparatus of claim 1, wherein thecomputing system is configured to perform automatic tagging thatcomprises linking an event of the user to the health metric predicted bythe computing system.
 10. The apparatus of claim 9, wherein the eventcomprises an exercising state of the user determined by the computingsystem or manually entered.
 11. The apparatus of claim 9, wherein theevent comprises a resting state of the user determined by the computingsystem or manually entered.
 12. The apparatus of claim 9, wherein theevent comprises an eating state of the user determined by the computingsystem or manually entered.
 13. An apparatus, comprising: a strap; acircuit board attached to the strap and comprising a plurality ofsensors, wherein the plurality of sensors comprises respective sensorsconfigured to sense electrocardiogram (ECG) data andphotoplethysmography (PPG) data from a user; and a computing systemcommunicatively coupled to the circuit board and configured to predict ahealth metric of the user by using machine learning and using the sensedECG data and the sensed PPG data as inputs for the machine learning,wherein the machine learning comprises a polynomial regression analysis,wherein the machine learning further comprises one or any combination ofa support vector machine and a neural network, wherein the circuit boardcomprises a light emitting diode (LED) and a light sensor, wherein theLED is configured to emit a first type of light and a second type oflight, wherein the light sensor comprises respective components to sensethe first type of light and the second type of light, wherein the firsttype of light has wavelengths within a first range of wavelengths andthe second type of light has wavelengths within a second range ofwavelengths, wherein the strap comprises openings, wherein the firsttype of light and the second type of light, or corresponding derivativesthereof, pass through the openings prior to being sensed by the lightsensor, wherein the light sensor and the LED are parts of a pulseoximeter that comprises the sensor configured to sense the PPG data,wherein the pulse oximeter comprises a computing component configuredto: compare a first reading sensed by the light sensor corresponding toan amount of the first type of light absorbed by blood of the user underskin of the user, corresponding to oxygenated blood, and a secondreading sensed by the light sensor corresponding to an amount of thesecond type of light absorbed by the blood, corresponding todeoxygenated blood; and determine an amount of oxygen in the blood basedon the comparison between the first reading and the second reading, andwherein the computing system is configured to predict the health metricof the user using the determined amount of oxygen in the blood as one ofthe inputs for the machine learning.
 14. The apparatus of claim 13,wherein the health metric comprises a blood pressure of the user. 15.The apparatus of claim 13, comprising: a first connector at a first endof the strap; and a second connector at a second end of the strap, thesecond connector configured to connect to the first connector to fastenthe first end to the second end, directly, to configure the strap into awristband, or indirectly, to configure the strap with a smartwatch intoa smart wristwatch.
 16. The apparatus of claim 13, further comprising: abattery charging circuit; and a microcontroller comprising the computingsystem, wherein the microcontroller is intermediate to the plurality ofsensors and the charging circuit.
 17. The apparatus of claim 13, whereinthe machine learning comprises supervised learning.
 18. An apparatus,comprising: a strap; a circuit board attached to the strap andcomprising a plurality of sensors, wherein the plurality of sensorscomprises respective sensors configured to sense electrocardiogram (ECG)data and photoplethysmography (PPG) data from a user; and a computingsystem communicatively coupled to the circuit board and configured topredict a health metric of the user by using machine learning and usingthe sensed ECG data, the sensed PPG data, and historical health data ofthe user as inputs for the machine learning, wherein the machinelearning comprises a support vector machine, wherein the machinelearning further comprises one or any combination of a polynomialregression analysis and a neural network, wherein the circuit boardcomprises a light emitting diode (LED) and a light sensor, wherein theLED is configured to emit a first type of light and a second type oflight, wherein the light sensor comprises respective components to sensethe first type of light and the second type of light, wherein the firsttype of light has wavelengths within a first range of wavelengths andthe second type of light has wavelengths within a second range ofwavelengths, wherein the strap comprises openings, wherein the firsttype of light and the second type of light, or corresponding derivativesthereof, pass through the openings prior to being sensed by the lightsensor, wherein the light sensor and the LED are parts of a pulseoximeter that comprises the sensor configured to sense the PPG data,wherein the pulse oximeter comprises a computing component configuredto: compare a first reading sensed by the light sensor corresponding toan amount of the first type of light absorbed by blood of the user underskin of the user, corresponding to oxygenated blood, and a secondreading sensed by the light sensor corresponding to an amount of thesecond type of light absorbed by the blood, corresponding todeoxygenated blood; and determine an amount of oxygen in the blood basedon the comparison between the first reading and the second reading, andwherein the computing system is configured to predict the health metricof the user using the determined amount of oxygen in the blood as one ofthe inputs for the machine learning.
 19. The apparatus of claim 18,wherein the health metric comprises a blood pressure of the user. 20.The apparatus of claim 18, further comprising: a battery chargingcircuit; and a microcontroller comprising the computing system, whereinthe microcontroller is intermediate to the plurality of sensors and thecharging circuit.